In this paper, we consider estimation of the conditional mode of an outcome
variable given regressors. To this end, we propose and analyze a
computationally scalable estimator derived from a linear quantile regression
model and develop asymptotic distributional theory for the estimator.
Specifically, we find that the pointwise limiting distribution is a scale
transformation of Chernoff's distribution despite the presence of regressors.
In addition, we consider analytical and subsampling-based confidence intervals
for the proposed estimator. We also conduct Monte Carlo simulations to assess
the finite sample performance of the proposed estimator together with the
analytical and subsampling confidence intervals. Finally, we apply the proposed
estimator to predicting the net hourly electrical energy output using Combined
Cycle Power Plant Data.

本文提出和分析了一种基于线性分位数回归模型的条件模估计量，并开发了其渐近分布理论。同时构建了分析和子采样法置信区间，并通过蒙特卡洛模拟评估了估计量和置信区间的有限样本性能。最后，将该估计量运用于预测联合循环发电厂数据的净小时电能输出。